1. Field of the Invention
This invention relates generally to the field of customer self service systems for resource search and selection, and more specifically, to a novel mechanism for classifying user contexts for facilitating a more focused search and improving the relevance of query results for such a system.
2. Discussion of the Prior Art
Currently there exist many systems designed to perform search and retrieval functions. These systems may be classified variously as knowledge management systems, information portals, search engines, data miners, etc. However, providing effective customer self service systems for resource search and selection presents several significant challenges. The first challenge for current systems with query capability is that serving queries intelligently requires a large amount of user supplied contextual information, while at the same time the user has limited time, patience, ability and interest to provide it. The second challenge is that searching without sufficient context results in a very inefficient search (both user time and system resource intensive) with frequently disappointing results (overwhelming amount of information, high percentage of irrelevant information). The third challenge is that much of a user's actual use and satisfaction with search results differ from that defined at the start of the search: either because the users behave contrary to their own specifications, or because there are other contextual issues at play that have not been defined into the search.
The prior art has separately addressed the use of the history of interaction with the user or their current service environment to provide context for a resource search and selection system. The prior art also assumes the shallow context of a single user query stream focused on a single topic. A major limitation of these approaches is that they burden the user with providing substantial contextual information and, further that these systems are unable to apply specific user context to improve resource selection for other users on the same subject.
As will be hereinafter explained in greater detail, some representative prior art search and retrieval systems include U.S. Pat. No. 5,974,412 entitled “Intelligent Query System for Automatically Indexing Information in a Database and Automatically Categorizing Users”; U.S. Pat. No. 5,600,835 entitled “Adaptive Non-Literal Text String Retrieval”; and U.S. Pat. No. 5,794,178 entitled “Visualization of Information Using Graphical Representations of Context Vector Based Relationships and Attributes”.
U.S. Pat. No. 5,974,412 describes an adaptive retrieval system that uses a vector of document and query features to drive the retrieval process. Specifically described is an Intelligent Query Engine (IQE) system that develops multiple information spaces in which different types of real-world objects (e.g., documents, users, products) can be represented. Machine learning techniques are used to facilitate automated emergence of information spaces in which objects are represented as vectors of real numbers. The system then delivers information to users based upon similarity measures applied to the representation of the objects in these information spaces. The system simultaneously classifies documents, users, products, and other objects with documents managed by collators that act as classifiers of overlapping portions of the database of documents. Collators evolve to meet the demands for information delivery expressed by user feedback. Liaisons act on the behalf of users to elicit information from the population of collators. This information is then presented to users upon logging into the system via Internet or another communication channel.
U.S. Pat. No. 5,600,835 describes a method and system for selectively retrieving information contained in a stored document set using a non-literal, or “fuzzy”, search strategy, and particularly implements an adaptive retrieval approach. A text string query is transmitted to a computer processor, and a dissimilarity value Di is assigned to selected ones of stored text strings representative of information contained in a stored document set, based upon a first set of rules. A set of retrieved text strings representative of stored information and related to the text string query is generated, based upon a second set of rules. Each of the retrieved text strings has an associated dissimilarity value Di, which is a function of at least one rule Rn from the first set of rules used to retrieve the text string and a weight value wn associated with that rule Rn. The retrieved text strings are displayed preferably in an order based on their associated dissimilarity value Di. Once one or more of the retrieved text strings is chosen, the weight value wn associated with at least one rule of the first set of rules is adjusted and stored.
U.S. Pat. No. 5,794,178 describes a system and method for automatically generating context vectors representing conceptual relationships among information items by quantitative means for use in storage and retrieval of documents and other information items and for displaying them visually to a user. A neural network operates on a training corpus of records to develop relationship-based context vectors based on word proximity and co-importance using a technique of “windowed co-occurrence”. Relationships among context vectors are deterministic, so that a context vector set has one logical solution, although it may have a plurality of physical solutions. No human knowledge, knowledge base, or conceptual hierarchy, is required. Summary vectors of records may be clustered to reduce searching time, by forming a tree of clustered nodes. Once the context vectors are determined, records may be retrieved using a query interface that allows a user to specify content terms, Boolean terms, and/or document feedback. Thus, context vectors are translated into visual and graphical representations to thereby provide user visualization of textual information and enable visual representations of meaning so that users may apply human pattern recognition skills to document searches.
It would be highly desirable to provide for a customer self service system, a mechanism that applies user context for the purpose of more efficient resource dispersion.
It would be further highly desirable to provide a mechanism for a customer self service system that applies user context for the purpose of more efficient resource dispersion and, that improves the relevance of search results for a given user in their current context without requiring the user to explicitly train the system.
It would be highly desirable to provide for a customer self service system, a mechanism that is able to improve the search value by including criteria derived from both data and behaviors in the general population which may be unknown to the user.